# A Joint and Domain-Adaptive Approach to Spoken Language Understanding

arXiv: https://arxiv.org/abs/2107.11768 · HTML: https://mlsanity.com/paper/2107.11768/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 36 references that are in the map.
- First posted on arXiv 25 Jul 2021.

## Builds on (most cited first, 26 of 26)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1905.08743/) (2019)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog](https://mlsanity.com/paper/1810.13327/) (2018)
- [A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding](https://mlsanity.com/paper/1909.02188/) (2019)
- [A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1907.00390/) (2019)
- [Deep Unknown Intent Detection with Margin Loss](https://mlsanity.com/paper/1906.00434/) (2019)
- [Zero-shot User Intent Detection via Capsule Neural Networks](https://mlsanity.com/paper/1809.00385/) (2018)
- [Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference](https://mlsanity.com/paper/2010.13009/) (2020)
- [SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/2010.02693/) (2020)
- [Towards Zero-Shot Frame Semantic Parsing for Domain Scaling](https://mlsanity.com/paper/1707.02363/) (2017)
- [Robust Zero-Shot Cross-Domain Slot Filling with Example Values](https://mlsanity.com/paper/1906.06870/) (2019)
- [Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling](https://mlsanity.com/paper/2004.11727/) (2020)
- [CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding](https://mlsanity.com/paper/1909.06937/) (2019)
- [OneNet: Joint Domain, Intent, Slot Prediction for Spoken Language Understanding](https://mlsanity.com/paper/1801.05149/) (2018)
- [Encoder-decoder with Focus-mechanism for Sequence Labelling Based Spoken Language Understanding](https://mlsanity.com/paper/1608.02097/) (2016)
- [Multi-Domain Adversarial Learning for Slot Filling in Spoken Language Understanding](https://mlsanity.com/paper/1711.11310/) (2017)
- [Multi-Domain Spoken Language Understanding Using Domain- and Task-Aware Parameterization](https://mlsanity.com/paper/2004.14871/) (2020)
- [Locale-agnostic Universal Domain Classification Model in Spoken Language Understanding](https://mlsanity.com/paper/1905.00924/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Unified Framework for Multi-intent Spoken Language Understanding with prompting](https://mlsanity.com/paper/2210.03337/) (2022)

## Abstract (arXiv)

Spoken Language Understanding (SLU) is composed of two subtasks: intent detection (ID) and slot filling (SF). There are two lines of research on SLU. One jointly tackles these two subtasks to improve their prediction accuracy, and the other focuses on the domain-adaptation ability of one of the subtasks. In this paper, we attempt to bridge these two lines of research and propose a joint and domain adaptive approach to SLU. We formulate SLU as a constrained generation task and utilize a dynamic vocabulary based on domain-specific ontology. We conduct experiments on the ASMixed and MTOD datasets and achieve competitive performance with previous state-of-the-art joint models. Besides, results show that our joint model can be effectively adapted to a new domain.